P573: NOVEL PROGNOSTIC MODEL FOR RELAPSE IN PATIENTS WITH ACUTE MYELOID LEUKEMIA RECEIVING HAPLOIDENTICAL HEMATOPOIETIC STEM CELL TRANSPLANTATION
Authors: Qi Wen, Shuang Fan, Hao-Yang Hong, Xiao-Hui Zhang, Lanping Xu, Yu Wang, Chen-Hua Yan, Huan Chen, Yu-Hong Chen, Wei Han, Feng-Rong Wang, Meng-Zhu Shen, Xiao-Jun Huang, Shen-Da Hong, Xiao-Dong Mo
Journal: HemaSphere (2023)

Background: Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the most important curative therapy for acute myeloid leukemia (AML), and haploidentical related donor (HID) has become the most important alternative donor with strong graft-versus-leukemia effect. However, relapse is still inevitable and is one of the most critical cause of transplant failure. Comprehensive prognostic models have been established to predict relapse in HID HSCT recipients. However, these studies included hematologic malignancies other than AML, and the primary endpoint was survival. Thus far, no comprehensive model could be used to predict the relapse of AML patients receiving HID HSCT in complete remission (CR). Aims: In the present study, we aimed to establish a comprehensive model with machine learning algorithm which could predict the risk of relapse in AML patients receiving HID HSCT in CR. Methods: A total of 951 patients were enrolled, then we randomly selected 70% of the entire population (n = 665) as the training cohort for developing machine learning model and the nomogram, and the remaining 30% (n = 286) as validation cohort. We used multivariate logistic regression analysis for feature selection. We included the variables whose coefficients with P values less than 0.1 as the input for the machine learning model. The machine learning model was evaluated among validation cohort. The accuracy, area under the curve (AUC) score, sensitivity, and specificity were computed in both data cohorts for model evaluation. In addition, the calibration curve and decision curve were plotted for identifying the usability of nomogram. Results: A total of five variables were included in the final model, and our equation was as follows: Probability (relapse) = $\ \frac{1}{1\+\exp(-Y)}$. Y = 0.5677 × (AML risk category at diagnosis) + 0.0690 × (induction courses for first CR) + 0.4583 × (disease status before HSCT) + 0.4061 × (multiparameter flow cytometry [MFC] before HSCT) – 0.1623 × (blood group disparity) – 2.9641. The threshold of probability was set as 0.1106, and the g-mean was 0.668. A nomogram was designed with a C-index of 0.707 (95% CI 0.645–0.770). The calibration plots revealed satisfactory agreement between nomogram prediction and actual observation for the probability of relapse at day. The optimal cutoff value of the total nomogram scores was determined to be 53 (Figure 1). The patients were separated into low- and high-risk groups. The 3-year cumulative incidence of relapse after HID HSCT was 19.0% (95% CI, 14.8%–23.2%) versus 8.2% (95% CI, 5.8%–10.6%) (Figure 2A, P < 0.001), 18.9% (95% CI, 13.8%–24.0%) versus 9.2% (95% CI, 6.1%–12.2%) (Figure 2B, P < 0.001), and 19.5% (95% CI, 11.9%–27.1%) versus 5.9% (95% CI, 2.1%–9.7%) (Figure 2C, P < 0.001), respectively, in the high- and low-risk groups in the total cohort, training cohort, and validation cohort. The 3-year probabilities of measurable residual disease occurrence, event-free survival, leukemia-free survival and overall survival in low-risk group were also significantly better than those in high-risk group. This model cod also predict the efficacy of preemptive immunotherapy after HID HSCT.Summary/Conclusion: In summary, we firstly established a comprehensive model to predict the relapse of AML patients receiving HID HSCT in CR, which can be popularized easily, can help to provide risk-stratification directed relapse prophylaxis, and may further decrease the risk of relapse. In future, prospective studies with independent cohort can further confirm the efficacy of our predicted model. Keywords: Haploidentical stem cell transplantation, AML, Relapse